Seasonal and diurnal variations in biogenic volatile organic compounds in highland and lowland ecosystems in southern Kenya

Abstract

Abstract. The East African lowland and highland areas consist of water-limited and humid ecosystems. The magnitude and seasonality of biogenic volatile organic compounds (BVOCs) emissions and concentrations from these functionally contrasting ecosystems are limited due to a scarcity of direct observations. We measured mixing ratios of BVOCs from two contrasting ecosystems, humid highlands with agroforestry and dry lowlands with bushland, grassland, and agriculture mosaics, during both the rainy and dry seasons of 2019 in southern Kenya. We present the diurnal and seasonal characteristics of BVOC mixing ratios and their reactivity and estimated emission factors (EFs) for certain BVOCs from the African lowland ecosystem based on field measurements. The most abundant BVOCs were isoprene and monoterpenoids (MTs), with isoprene contributing > 70 % of the total BVOC mixing ratio during daytime, while MTs accounted for > 50 % of the total BVOC mixing ratio during nighttime at both sites. The contributions of BVOCs to the local atmospheric chemistry were estimated by calculating the reactivity towards the hydroxyl radical (OH), ozone (O3), and the nitrate radical (NO3). Isoprene and MTs contributed the most to the reactivity of OH and NO3, while sesquiterpenes dominated the contribution of organic compounds to the reactivity of O3. The mixing ratio of isoprene measured in this study was lower than that measured in the relevant ecosystems in western and southern Africa, while that of monoterpenoids was similar. Isoprene mixing ratios peaked daily between 16:00 and 20:00 (all times are given as East Africa Time, UTC+3),​​​​​​​ with a maximum mixing ratio of 809 pptv (parts per trillion by volume) and 156 pptv in the highlands and 115 and 25 pptv in the lowlands during the rainy and dry seasons, respectively. MT mixing ratios reached their daily maximum between midnight and early morning (usually 04:00 to 08:00), with mixing ratios of 254 and 56 pptv in the highlands and 89 and 7 pptv in the lowlands in the rainy and dry seasons, respectively. The dominant species within the MT group were limonene, α-pinene, and β-pinene. EFs for isoprene, MTs, and 2-Methyl-3-buten-2-ol (MBO) were estimated using an inverse modeling approach. The estimated EFs for isoprene and β-pinene agreed very well with what is currently assumed in the world's most extensively used biogenic emissions model, the Model of Emissions of Gases and Aerosols from Nature (MEGAN), for warm C4 grass, but the estimated EFs for MBO, α-pinene, and especially limonene were significantly higher than that assumed in MEGAN for the relevant plant functional type. Additionally, our results indicate that the EF for limonene might be seasonally dependent in savanna ecosystems.

Emissions of biogenic volatile organic compounds from agricultural lands and the impact of land-use and other management practices: A review

“…The standard conditions were selected due to the key factors controlling plant and microbial production, respectively (Guenther et al, 1995;Tang et al, 2019). EFs for crop BVOC are currently applying the same standard conditions as that used for tree species (Guenther et al, 2012), and lots of studies on crop EFs were calculated by inversing the MEGAN model on an ecosystem or an individual plant scale (Bachy et al, 2016;Gonzaga-Gomez et al, 2019;Liu et al, 2021a). We summarized the crop EFs from published articles and presented the values in Table 5.…”

Measurement report: Long-term measurements of ozone concentrations in semi-natural African ecosystems

“…MEGAN (Model of Emissions of Gases and Aerosols from Nature) inventory quantifies net biogenic emissions of isoprene and other gases emitted by vegetation into the atmosphere (Guenther et al, 2006;Sindelarova et al, 2014). Isoprene, α-pinene and β-pinene, which account for the largest proportion of BVOCs emitted by vegetation in Africa (Ferreira et al, 2010;Jaars et al 2016;Liu et al, 2021;Saxton et al, 2007;Serça et al, 2001), were identified and used in this study. A more detailed description of these emission inventories is discussed in the work of García-Lázaro et al ( 2018), Guenther et al (2006) and Vitolo et al (2018).…”